To gain a better understanding of the global application of soil erosion prediction models, we comprehensively reviewed relevant peer-reviewed research literature on soil-erosion modelling published between 1994 and 2017. We aimed to identify (i) the processes and models most frequently addressed in the literature, (ii) the regions within which models are primarily applied, (iii) the regions which remain unaddressed and why, and (iv) how frequently studies are conducted to validate/evaluate model outcomes relative to measured data. To perform this task, we combined the collective knowledge of 67 soil-erosion scientists from 25 countries. The resulting database, named 'Global Applications of Soil Erosion Modelling Tracker (GASEMT)', includes 3030 individual modelling records from 126 countries, encompassing all continents (except Antarctica). Out of the 8471 articles identified as potentially relevant, we reviewed 1697 appropriate articles and systematically evaluated and transferred 42 relevant attributes into the database. This GASEMT database provides comprehensive insights into the state-of-the-art of soil- erosion models and model applications worldwide. This database intends to support the upcoming country-based United Nations global soil-erosion assessment in addition to helping to inform soil erosion research priorities by building a foundation for future targeted, in-depth analyses. GASEMT is an open-source database available to the entire user-community to develop research, rectify errors, and make future expansions.

Nitrogen (N) in the agricultural production system influences many aspects of agroecosystems and several critical ecosystem services widely depend on the N availability in the soil. Cumulative changes in regional ecosystem services may lead to global environmental changes. Thus, the soil N status in agriculture is of critical importance to strategize its most efficient use. Nitrogen is also one of the most susceptible macronutrients to environmental loss, such as ammonia volatilization (NH₃), nitrous oxide (N₂O) emissions, nitrate leaching (NO₃), etc. Any form of N losses from agricultural systems can be major limitations for crop production, soil sustainability, and environmental safeguard. There is a need to focus on mitigation strategies to minimize global N pollution and implement agricultural management practices that encourage regenerative and sustainable agriculture. In this review, we identified the avenues of N loss into the environment caused by current agronomic practices and discussed the potential practices that can be adapted to prevent this N loss in production agriculture. This review also explored the N status in agriculture during the COVID-19 pandemic and the existing knowledge gaps and questions that need to be addressed.

Organic farming, which is deeply rooted in traditional agricultural practices, has witnessed a profound evolution over the last century. Transitioning from a grassroots initiative resisting the industrialization of agriculture to a global industry, organic farming now plays a pivotal role in addressing contemporary challenges related to environmental health, sustainability, and food safety. Despite the growing consumer demand for organic products and market access, organic farming has its challenges. This paper discusses the origin and evolution of organic farming with an emphasis on different types of organic fertilizers, benefits, and challenges. Nutrient variability and the slow-release nature of organic fertilizer often do not meet crop demands and can substantially reduce yield. Some organic fertilizers, like manure and biosolids, can provide a higher yield benefit, but there are environmental and health risks associated with them. Weed and pest management in organic farming can be labor-intensive and increase costs. Inefficient planning of organic farming and rapid transition can also create food insecurity. This paper also gives a brief account of the current certification process for organic fertilizers and their technicalities. It showcases how the holistic approach of organic farming extends beyond production, including strategies like reducing food waste and building self-sufficient farming communities. These practices contribute to a more sustainable agricultural system, reducing environmental impacts and supporting local economies. Future technological innovations, especially in precision agriculture and bio-physicochemical models, can help in formulating targeted organic fertilizers.

Soil erosion can present a major threat to agriculture due to loss of soil, nutrients, and organic carbon. Therefore, soil erosion modelling is one of the steps used to plan suitable soil protection measures and detect erosion hotspots. A bibliometric analysis of this topic can reveal research patterns and soil erosion modelling characteristics that can help identify steps needed to enhance the research conducted in this field. Therefore, a detailed bibliometric analysis, including investigation of collaboration networks and citation patterns, should be conducted. The updated version of the Global Applications of Soil Erosion Modelling Tracker (GASEMT) database contains information about citation characteristics and publication type. Here, we investigated the impact of the number of authors, the publication type and the selected journal on the number of citations. Generalized boosted regression tree (BRT) modelling was used to evaluate the most relevant variables related to soil erosion modelling. Additionally, bibliometric networks were analysed and visualized. This study revealed that the selection of the soil erosion model has the largest impact on the number of publication citations, followed by the modelling scale and the publication's CiteScore. Some of the other GASEMT database attributes such as model calibration and validation have negligible influence on the number of citations according to the BRT model. Although it is true that studies that conduct calibration, on average, received around 30% more citations, than studies where calibration was not performed. Moreover, the bibliographic coupling and citation networks show a clear continental pattern, although the co-authorship network does not show the same characteristics. Therefore, soil erosion modellers should conduct even more comprehensive review of past studies and focus not just on the research conducted in the same country or continent. Moreover, when evaluating soil erosion models, an additional focus should be given to field measurements, model calibration, performance assessment and uncertainty of modelling results. The results of this study indicate that these GASEMT database attributes had smaller impact on the number of citations, according to the BRT model, than anticipated, which could suggest that these attributes should be given additional attention by the soil erosion modelling community. This study provides a kind of bibliographic benchmark for soil erosion modelling research papers as modellers can estimate the influence of their paper.

Digital soil mapping has been widely used to develop statistical models of the relationships between environmental variables and soil attributes. This study aimed at determining and mapping the spatial distribution of the variability in soil chemical properties of the agricultural floodplain lands of the Bara district in Nepal. The study was carried out in 23 Village Development Committees with 12,516 ha total area, in the southern part of the Bara district. A total of 109 surface soil samples (0 to 15 cm depth) were collected and analyzed for pH, organic matter (OM), nitrogen (N), phosphorus (P, expressed as P₂O₅), potassium (K, expressed as K₂O), zinc (Zn), and boron (B) status. Descriptive statistics showed that most of the measured soil chemical variables (other than pH and P₂O₅) were skewed and non-normally distributed and logarithmic transformation was then applied. A geostatistical tool, kriging, was used in ArcGIS to interpolate measured values for those variables and several digital map layers were developed based on each soil chemical property. Geostatistical interpolation identified a moderate spatial variability for pH, OM, N, P₂O₅, and a weak spatial variability for K₂O, Zn, and B, depending upon the use of amendments, fertilizing methods, and tillage, along with the inherent characteristics of each variable. Exponential (pH, OM, N, and Zn), Spherical (K₂O and B), and Gaussian (P₂O₅) models were fitted to the semivariograms of the soil variables. These maps allow farmers to assess existing farm soils, thus allowing them to make easier and more efficient management decisions and maintain the sustainability of productivity.